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Proceedings/Recueil des communications Année : 2020

Machine learning for optimized buildings morphosis

Résumé

The world is rapidly urbanizing, with an increasing number of new building constructions. This involves increasing the world's energy consumption and its associated greenhouse gas emissions. Computational tools are playing an increasing impact on the architectural design process. Recently, Machine learning (ML) has been applied to building design and has evinced its potential to improve building performance. This paper tries to review the use of ML for the building morphosis. We then forecast the use of machine learning for building optimized morphosis in the early design stage particularly for ensuring summer shading and winter solar access between neighbors.
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Dates et versions

hal-03258021, version 1 (11-06-2021)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification - CC BY 4.0

Identifiants

Citer

Khaoula Raboudi, Abdelkader Ben Saci. Machine learning for optimized buildings morphosis. E. Reyes, G. Kembellec, F. Siala-Kallel, L. Sfaxi, M. Ghenima, I. Saleh. DTUC '20: Digital Tools & Uses Congress, Oct 2020, Virtual Event Tunisia, Proceedings of the 2nd International Conference, Association for Computing Machinery, pp.1-5, 2020, 978-1-4053-7753-9. ⟨10.1145/3423603.3424057⟩. ⟨hal-03258021⟩
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